Editing Causal Inference in AI (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
The fundamental problem of causal inference is what statistician Donald Rubin called the '''Fundamental Problem of Causal Inference''': we can never observe both potential outcomes for the same unit at the same time. Either a patient received the drug (Y(1) observed, Y(0) unobserved) or they didn't (Y(0) observed, Y(1) unobserved). We can never know what would have happened to the same person under the alternative treatment — the counterfactual.

Judea Pearl's '''Ladder of Causation''' describes three levels of causal reasoning:
1. '''Association''' (rung 1): "What is?" — Observing and predicting correlations. Standard ML lives here.
2. '''Intervention''' (rung 2): "What if I do X?" — Reasoning about the effect of deliberate actions. Requires a causal model.
3. '''Counterfactual''' (rung 3): "What if I had done X instead?" — Imagining alternate histories. Requires a complete structural causal model.

Most ML systems operate only on rung 1. To make reliable decisions and avoid discrimination, AI systems often need rung 2 or 3.

'''Why this matters for AI''':
* '''Spurious correlations''': A model that classifies "pneumonia" as lower risk may have learned that pneumonia patients sent to the ICU have lower final mortality — confusing treatment effect with baseline risk.
* '''Fairness''': Is a model discriminating based on race, or is it using variables that are correlated with race but causally related to the outcome? Causal fairness criteria give precise answers.
* '''Policy decisions''': If we deploy an AI to recommend interventions, we must understand the causal effect of those interventions — not just their correlation with past outcomes.
* '''Robustness''': Models that learn causal relationships rather than spurious correlations generalize better when the environment changes (distribution shift).
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